12 Best Data Classification Software and Tools for 2026

Data classification software finds sensitive information across your files, databases, cloud storage and email, and labels it by sensitivity (for example public, internal, confidential or restricted) so that other controls can protect it. Those labels drive encryption, data loss prevention, access rules and retention, which is why classification is the foundation of most data security programmes.

The category now spans three approaches. There are user-driven labelling tools that prompt people to classify documents and emails as they create them, automated discovery platforms that scan data stores with pattern matching and machine learning, and newer data security posture management (DSPM) tools built for cloud and AI data. The market has also consolidated: Veeam completed its $1.725 billion acquisition of Securiti AI in December 2025, and Varonis is moving to a SaaS-only model.

Common classification levels, and how they map to government schemes

Most organisations use four levels:

How we evaluated these tools

Data classification software at a glance

12 best data classification tools reviewed

1. Microsoft Purview Information Protection

Best for: organisations standardised on Microsoft 365.

Purview provides sensitivity labels that users apply in Office apps and Outlook, plus automatic labelling based on sensitive information types and trainable classifiers. Labels can encrypt content, add markings and drive data loss prevention across Microsoft 365. The Purview information protection scanner extends discovery and labelling to on-premise file shares and SharePoint Server.

Key features

• Sensitivity labels built into Office and Outlook
• Automatic labelling with sensitive information types and trainable classifiers
• Encryption and visual markings driven by labels
• Scanner for on-premise file shares

Deployment: cloud, with an on-premise scanner.

Pricing: manual labelling is included in many Microsoft 365 plans; automatic labelling generally needs E5 or E5 Compliance licences [VERIFY].

Pros

• Native to the tools people already use
• Labels travel with documents

Cons

• Best features need premium licences
• Less coverage outside Microsoft

Not for: organisations that do not use Microsoft 365.

2. Varonis

Best for: organisations with large volumes of unstructured data on file servers and SaaS platforms.

Varonis classifies sensitive data and maps who can access it, then monitors activity to detect abnormal behaviour and automatically removes excessive permissions. It has moved to a SaaS-only model. A competitor’s analysis reports that support for the self-hosted platform ends on 31 December 2026 [VERIFY], so existing on-premise customers must migrate or replace it.

Key features

• Classification combined with permission analysis
• Automated least-privilege remediation
• Behaviour monitoring and threat detection
• Coverage of Microsoft 365, Google, Salesforce and file servers

Deployment: SaaS (self-hosted platform being retired).

Pricing: on request.

Pros

• Connects sensitivity with access and activity
• Strong automation

Cons

• No long-term self-hosted option
• Premium pricing

Not for: organisations that must keep the platform on-premise.

3. BigID

Best for: large enterprises that need discovery across every kind of data store.

BigID uses machine learning to discover, classify and catalogue sensitive and personal data across structured databases, unstructured files, cloud and SaaS. It supports privacy, security and AI data governance use cases from one platform.

Key features

• ML-based classification and identity correlation
• Coverage of structured and unstructured data
• Privacy and data subject request workflows
• AI data governance features

Deployment: cloud or self-hosted.

Pricing: on request.

Pros

• Very broad data source coverage
• Self-hosted option

Cons

• Complex to deploy fully
• Enterprise pricing

Not for: small organisations.

4. Forcepoint Data Classification

Best for: organisations using Forcepoint data loss prevention.

Forcepoint’s AI-powered classification identifies sensitive unstructured data and feeds those results into Forcepoint DLP and its broader data security platform, so policies act on accurate classifications.

Key features

• AI-powered classification
• Integration with Forcepoint DLP
• Data security posture visibility
• Policy-driven protection

Deployment: cloud.

Pricing: on request.

Pros

• Tight link to DLP enforcement
• Mature data security vendor

Cons

• Best value inside the Forcepoint stack
• Pricing not published

Not for: teams not using Forcepoint.

5. Netwrix Data Classification

Best for: mid-size organisations with Windows file servers and SharePoint.

Netwrix discovers and classifies sensitive data on file shares, SharePoint, Microsoft 365 and databases, and can apply Microsoft sensitivity labels, move or quarantine files, and remediate access as part of the wider Netwrix platform.

Key features

• Discovery on file shares, SharePoint and databases
• Applies Microsoft sensitivity labels
• Automated remediation actions
• Integration with Netwrix auditing

Deployment: on-premise.

Pricing: on request.

Pros

• On-premise deployment
• Good fit for Microsoft-centric estates

Cons

• Interface less modern
• Pricing not published

Not for: cloud-only organisations.

6. Fortra Data Classification

Best for: organisations that want employees to label documents and emails as they work.

Fortra’s data classification suite brings together the Titus and Boldon James products. They add classification prompts and visual markings to Office documents and email, apply metadata that downstream tools can read, and combine user decisions with automatic suggestions. They are widely used in defence and government.

Key features

• User-driven labelling in Outlook and Office
• Visual markings and metadata
• Automatic suggestions
• Government marking schemes supported

Deployment: on-premise or cloud.

Pricing: on request.

Pros

• Strong in defence and government
• Builds a classification culture

Cons

• Relies partly on user decisions
• Product naming has changed

Not for: teams that want fully automatic classification only.

7. Spirion

Best for: organisations that need precise discovery of personal and regulated data.

Spirion focuses on accurate discovery of sensitive data, including personal, financial and health data, across endpoints, servers, databases and cloud, with automated classification and remediation.

Key features

• High-precision sensitive data discovery
• Endpoint and server scanning
• Automated classification and remediation
• Privacy reporting

Deployment: cloud or on-premise.

Pricing: on request.

Pros

• Low false-positive focus
• Endpoint coverage

Cons

• Smaller ecosystem
• Pricing not published

Not for: organisations wanting a broad data security suite.

8. Securiti (Veeam)

Best for: organisations combining privacy, data security and AI governance.

Securiti built a “data command centre” covering discovery, classification, privacy automation, DSPM and AI data governance across multi-cloud, SaaS and on-premise data. Veeam completed its acquisition of Securiti AI in December 2025, combining it with Veeam’s data resilience platform.

Key features

• Discovery and classification across hybrid data
• Privacy and consent automation
• DSPM
• AI data governance

Deployment: cloud.

Pricing: on request.

Pros

• Very broad scope
• Strong for privacy teams

Cons

• Integration into Veeam is ongoing
• Complex for narrow needs

Not for: teams that only need simple labelling.

9. Nightfall AI

Best for: protecting sensitive data in SaaS apps and generative AI tools.

Nightfall uses AI models to detect sensitive data in SaaS applications such as Slack, Google Workspace and Jira, in browsers and generative AI prompts, and on endpoints, then blocks, redacts or alerts.

Key features

• AI detection of secrets, PII and financial data
• Coverage of SaaS and GenAI tools
• Real-time redaction and blocking
• Endpoint and browser protection

Deployment: cloud.

Pricing: on request.

Pros

• Strong for SaaS and AI tools
• Fast deployment

Cons

• Less suited to on-premise file servers
• Cloud only

Not for: air-gapped environments.

10. Cyera

Best for: cloud-first organisations adopting DSPM.

Cyera is a data security posture management platform that connects agentlessly to cloud and SaaS data stores, discovers and classifies data with AI, and highlights exposure risks such as public access or excessive permissions.

Key features

• Agentless cloud data discovery
• AI classification
• Exposure and access risk analysis
• Coverage of IaaS, PaaS and SaaS

Deployment: cloud.

Pricing: on request.

Pros

• Fast time to value in cloud
• Modern DSPM approach

Cons

• Less on-premise coverage
• Pricing not published

Not for: on-premise-heavy estates.

11. ManageEngine DataSecurity Plus

Best for: budget-conscious teams classifying Windows file server data on-premise.

DataSecurity Plus combines file server auditing, sensitive data discovery and classification, and file analysis, deployed on-premise. It is a practical option for organisations that need visibility into file shares without an enterprise budget.

Key features

• Sensitive data discovery and classification
• File server auditing
• File analysis and storage optimisation
• On-premise deployment

Deployment: on-premise.

Pricing: published on the ManageEngine website.

Pros

• Low cost
• Fully on-premise

Cons

• Focused on Windows file servers
• Limited cloud coverage

Not for: cloud-first organisations.

12. OvalEdge

Best for: data teams that want classification inside a data catalogue.

OvalEdge is a data catalogue and governance platform that automatically classifies sensitive data in databases and data lakes, tags it, and connects it to access policies and data lineage.

Key features

• Data catalogue with automatic classification
• Lineage and glossary
• Access governance
• Cloud or on-premise

Deployment: cloud or on-premise.

Pricing: on request.

Pros

• Combines governance and classification
• On-premise option

Cons

• Focused on structured data
• Less suited to documents and email

Not for: teams mainly classifying documents.

Classified data in chat: the gap most programmes miss

Most classification programmes cover files and email well. Chat is the blind spot. Engineers paste configuration details into threads, managers share screenshots of financials, and project teams discuss sensitive designs, often in SaaS chat tools that classification and DLP tools only partly see. Labels rarely travel into chat, and once content is posted, it is stored on the chat provider’s servers.

Close the gap in three ways:

• Extend classification policy to chat. Define what may never be posted, and train people on it.
• Use DLP that covers your chat platform, where available.
• Keep the chat platform inside your perimeter for sensitive work. Defence and intelligence organisations often run messaging on their own servers or in air-gapped networks, so classified conversations never reach a third-party cloud. Troop Messenger for defence is built for that model, with on-premise and air-gapped deployment and end-to-end encryption.

Classification in on-premise and air-gapped environments

Many newer DSPM tools are cloud-only. If your data, or the classification platform itself, must stay inside your network, shortlist tools with on-premise deployment: BigID (self-hosted), Netwrix, Fortra, Spirion, ManageEngine DataSecurity Plus and OvalEdge, plus the Microsoft Purview scanner for file shares. Note that Varonis is ending its self-hosted platform. For air-gapped networks, check how classifier and pattern updates are delivered offline.

How to choose data classification software

• Define your levels and map them to legal and regulatory obligations.
• Inventory data stores: file servers, Microsoft 365, cloud storage, databases, SaaS and chat.
• Decide between user-driven and automatic labelling, or a mix.
• Test accuracy on a sample of your own data and measure false positives.
• Check that labels drive action in your DLP, encryption and access controls.
• Confirm deployment for on-premise and isolated environments.
• Plan for AI tools: make sure classification covers what employees paste into AI assistants.
• Compare three-year cost, including data volume pricing.

Frequently asked questions

1. What are the best data classification tools?

Leading options include Microsoft Purview Information Protection for Microsoft 365, Varonis and Netwrix for file servers, BigID and Securiti for large-scale discovery, Fortra (Titus and Boldon James) for user-driven labelling, and Cyera and Nightfall for cloud and SaaS data. ManageEngine DataSecurity Plus is a lower-cost on-premise option.

2. What are the four types of data classification?

Most organisations use four sensitivity levels: public, internal, confidential and restricted. Public data can be shared freely, internal data is for employees only, confidential data would cause harm if disclosed, and restricted data, such as trade secrets or health records, needs the strongest protection.

3. What are the five common data classification levels?

Some organisations add a fifth level to the usual four, splitting restricted data into restricted and highly restricted or top secret. A common five-level scheme is public, internal, confidential, restricted and highly restricted. Governments use their own formal schemes, defined in law and policy.

4. What is the difference between data classification and DLP?

Data classification identifies and labels sensitive information. Data loss prevention (DLP) enforces rules that stop that information from leaving the organisation inappropriately, for example by blocking an email or upload. DLP works far better when it can rely on accurate classification labels.

5. Can data classification tools label chat messages?

Partly. Some tools, such as Nightfall and Microsoft Purview, can detect sensitive data in supported chat platforms and apply DLP policies, but labelling individual messages is less mature than labelling documents. Many sensitive organisations also keep chat on their own servers to limit exposure.